Overview

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory266.9 KiB
Average record size in memory92.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly correlated with invoice_no and 2 other fieldsHigh correlation
recency_days is highly correlated with invoice_noHigh correlation
invoice_no is highly correlated with gross_revenue and 2 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
aux_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_ticketHigh correlation
gross_revenue is highly correlated with invoice_no and 1 other fieldsHigh correlation
invoice_no is highly correlated with gross_revenue and 1 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with cant_returns and 1 other fieldsHigh correlation
cant_returns is highly correlated with avg_ticket and 1 other fieldsHigh correlation
aux_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with invoice_no and 1 other fieldsHigh correlation
invoice_no is highly correlated with gross_revenue and 1 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
gross_revenue is highly correlated with invoice_no and 4 other fieldsHigh correlation
invoice_no is highly correlated with gross_revenue and 1 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 2 other fieldsHigh correlation
cant_returns is highly correlated with gross_revenue and 2 other fieldsHigh correlation
aux_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362) Skewed
cant_returns is highly skewed (γ1 = 50.89499407) Skewed
aux_basket_size is highly skewed (γ1 = 44.67271661) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
cant_returns has 1480 (49.8%) zeros Zeros

Reproduction

Analysis started2023-02-18 14:10:59.864730
Analysis finished2023-02-18 14:11:36.330402
Duration36.47 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.277198
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:36.587247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.964441
Coefficient of variation (CV)0.67103083
Kurtosis-1.010787266
Mean2317.277198
Median Absolute Deviation (MAD)1271
Skewness0.3422499487
Sum6879996
Variance2417914.414
MonotonicityStrictly increasing
2023-02-18T15:11:36.794125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30111
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
30081
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2023-02-18T15:11:37.004999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2023-02-18T15:11:37.208874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.321711
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:37.446734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.62331
Coefficient of variation (CV)3.848448607
Kurtosis353.944724
Mean2749.321711
Median Absolute Deviation (MAD)672.16
Skewness16.77755612
Sum8162736.16
Variance111949589.6
MonotonicityNot monotonic
2023-02-18T15:11:37.687589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.962
 
0.1%
533.332
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
1314.452
 
0.1%
1353.742
 
0.1%
3312
 
0.1%
Other values (2944)2949
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28763894
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:37.951432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75677911
Coefficient of variation (CV)1.209513686
Kurtosis2.777962659
Mean64.28763894
Median Absolute Deviation (MAD)26
Skewness1.798379538
Sum190870
Variance6046.116697
MonotonicityNot monotonic
2023-02-18T15:11:38.216272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

invoice_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723139104
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:38.482113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85653132
Coefficient of variation (CV)1.547495379
Kurtosis190.8344494
Mean5.723139104
Median Absolute Deviation (MAD)2
Skewness10.76680458
Sum16992
Variance78.43814702
MonotonicityNot monotonic
2023-02-18T15:11:38.719501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

quantity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.63523072
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:38.950364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median11
Q314
95-th percentile22
Maximum102
Range101
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.273848163
Coefficient of variation (CV)0.5392113243
Kurtosis25.33565007
Mean11.63523072
Median Absolute Deviation (MAD)3
Skewness3.09936948
Sum34545
Variance39.36117078
MonotonicityNot monotonic
2023-02-18T15:11:39.231195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10292
 
9.8%
9258
 
8.7%
11252
 
8.5%
12221
 
7.4%
8218
 
7.3%
7210
 
7.1%
13199
 
6.7%
14165
 
5.6%
6157
 
5.3%
15141
 
4.7%
Other values (39)856
28.8%
ValueCountFrequency (%)
119
 
0.6%
232
 
1.1%
360
 
2.0%
482
 
2.8%
5105
 
3.5%
6157
5.3%
7210
7.1%
8218
7.3%
9258
8.7%
10292
9.8%
ValueCountFrequency (%)
1021
 
< 0.1%
741
 
< 0.1%
591
 
< 0.1%
581
 
< 0.1%
571
 
< 0.1%
561
 
< 0.1%
541
 
< 0.1%
502
0.1%
493
0.1%
444
0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.89776151
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:39.481045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.95658654
Q324.98828571
95-th percentile90.497
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86895238

Descriptive statistics

Standard deviation1036.934407
Coefficient of variation (CV)19.98033011
Kurtosis2890.707126
Mean51.89776151
Median Absolute Deviation (MAD)5.984842033
Skewness53.44422362
Sum154084.4539
Variance1075232.964
MonotonicityNot monotonic
2023-02-18T15:11:39.682927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2956)2956
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1257
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.34805267
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:39.896799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92307692
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.41025641

Descriptive statistics

Standard deviation63.54523638
Coefficient of variation (CV)0.9435348739
Kurtosis4.887024667
Mean67.34805267
Median Absolute Deviation (MAD)26.28571429
Skewness2.062752894
Sum199956.3684
Variance4037.997067
MonotonicityNot monotonic
2023-02-18T15:11:40.100676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
1117
 
0.6%
4617
 
0.6%
2117
 
0.6%
4216
 
0.5%
Other values (1247)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1350
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06327876079
Minimum0.005449591281
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:40.316545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.009433962264
Q10.01777777778
median0.02941176471
Q30.05540166205
95-th percentile0.2222222222
Maximum3
Range2.994550409
Interquartile range (IQR)0.03762388427

Descriptive statistics

Standard deviation0.1344820182
Coefficient of variation (CV)2.125231539
Kurtosis121.5575391
Mean0.06327876079
Median Absolute Deviation (MAD)0.01433823529
Skewness8.773253202
Sum187.8746408
Variance0.01808541321
MonotonicityNot monotonic
2023-02-18T15:11:40.520426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.166666666721
 
0.7%
0.333333333321
 
0.7%
0.0277777777820
 
0.7%
0.0909090909119
 
0.6%
0.062517
 
0.6%
0.133333333316
 
0.5%
0.416
 
0.5%
0.0357142857115
 
0.5%
0.0238095238115
 
0.5%
0.2515
 
0.5%
Other values (1340)2794
94.1%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
21
 
< 0.1%
1.5714285711
 
< 0.1%
1.53
 
0.1%
114
0.5%
0.83333333331
 
< 0.1%
0.751
 
< 0.1%
0.666666666712
0.4%
0.65147453081
 
< 0.1%
0.61
 
< 0.1%

cant_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct215
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.30953183
Minimum0
Maximum80995
Zeros1480
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:40.736296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile102.2
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1522.090875
Coefficient of variation (CV)23.30579982
Kurtosis2696.42626
Mean65.30953183
Median Absolute Deviation (MAD)1
Skewness50.89499407
Sum193904
Variance2316760.633
MonotonicityNot monotonic
2023-02-18T15:11:40.948169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
49.8%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (205)707
23.8%
ValueCountFrequency (%)
01480
49.8%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
93601
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%

aux_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.8137641
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:41.179030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.3333333
Q3281.6923077
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.4423077

Descriptive statistics

Standard deviation791.5551894
Coefficient of variation (CV)3.168581172
Kurtosis2255.538236
Mean249.8137641
Median Absolute Deviation (MAD)83.08333333
Skewness44.67271661
Sum741697.0657
Variance626559.6179
MonotonicityNot monotonic
2023-02-18T15:11:41.399899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
829
 
0.3%
869
 
0.3%
1368
 
0.3%
758
 
0.3%
608
 
0.3%
888
 
0.3%
1057
 
0.2%
Other values (1969)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.1547082
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-02-18T15:11:41.621763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.345454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.51232207
Coefficient of variation (CV)0.8807302672
Kurtosis27.70329723
Mean22.1547082
Median Absolute Deviation (MAD)8.2
Skewness3.499455899
Sum65777.32865
Variance380.7307127
MonotonicityNot monotonic
2023-02-18T15:11:41.825644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353
 
1.8%
1439
 
1.3%
1138
 
1.3%
933
 
1.1%
2033
 
1.1%
132
 
1.1%
1731
 
1.0%
1030
 
1.0%
1830
 
1.0%
1629
 
1.0%
Other values (995)2621
88.3%
ValueCountFrequency (%)
132
1.1%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
224
0.8%
ValueCountFrequency (%)
299.70588241
< 0.1%
2591
< 0.1%
203.51
< 0.1%
1481
< 0.1%
1451
< 0.1%
136.1251
< 0.1%
135.51
< 0.1%
1271
< 0.1%
1221
< 0.1%
1181
< 0.1%

Interactions

2023-02-18T15:11:33.034538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:04.879110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:08.353483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:10.634115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:13.079650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:15.539183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:17.791830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:20.231371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:23.067697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:25.578193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:27.839628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:30.264933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:33.227423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:06.027340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:08.534374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:11.022885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:13.274536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:15.727067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:17.983717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:20.430252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:23.351525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:25.766082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:28.042506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:30.461816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:33.411314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:06.247211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:08.722260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:11.203774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:13.456425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:15.952930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:18.163607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:20.665111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:23.583386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:25.954967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:28.244141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:30.651702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:33.602200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:06.481069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:08.904152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:11.381668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:13.646314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:16.129826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:18.341501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:20.893973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:23.772275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:26.140855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:28.441025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:31.136673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:33.794083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:06.683480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:09.094039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:11.572557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:13.850189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:16.345697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:18.525391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:21.131830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:23.991143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:26.336738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:28.647901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:31.373532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:33.973978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:06.874366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:09.268932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:11.747448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:14.025086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:16.512598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:18.688294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:21.317720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:24.198021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:26.507637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:28.838786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:31.564419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:34.153870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:07.103229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:09.455821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:11.924347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:14.211975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:16.681497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:18.861192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:21.599552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:24.385906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:26.691526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:29.035669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:31.755305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:34.345752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:07.346083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:09.633716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:12.107233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:14.394863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:16.863388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:19.041082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:21.825415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:24.575794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:26.870420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:29.231553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:31.947189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:34.543636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:07.552960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:09.859578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:12.304115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:14.590748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:17.045277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:19.232969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:22.133231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:24.771677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:27.061308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:29.440428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:32.196041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:34.733522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:07.748842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:10.047468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:12.491004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:14.784632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:17.222174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:19.413861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:22.354099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:24.964562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:27.253192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:29.644305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:32.408912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:34.935401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:07.949722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:10.245349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:12.687888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:15.004502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:17.420053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:19.840603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:22.639929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:25.171439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:27.452071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:29.850180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:32.618787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:35.145277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:08.158597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:10.444232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:12.890766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:15.316312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:17.609939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:20.049478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:22.856821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:25.380313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:27.654738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:30.064051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-18T15:11:32.835656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-02-18T15:11:42.405799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-18T15:11:42.760948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-18T15:11:43.050776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-18T15:11:43.342602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-18T15:11:35.529878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T15:11:35.943944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketavg_recency_daysfrequencycant_returnsaux_basket_sizeavg_unique_basket_size
00178505391.21372.034.06.018.15222235.5000000.48611140.050.9705888.735294
11130473232.5956.09.011.018.90403527.2500000.04878035.0154.44444419.000000
22125836705.382.015.024.028.90250023.1875000.04569950.0335.20000015.466667
3313748948.2595.05.08.033.86607192.6666670.0179210.087.8000005.600000
4415100876.00333.03.02.0292.0000008.6000000.13636422.026.6666671.000000
55152914623.3025.014.017.045.32647123.2000000.05444129.0150.1428577.285714
66146885630.877.021.024.017.21978618.3000000.073569399.0172.42857115.571429
77178095411.9116.012.023.088.71983635.7000000.03910641.0171.4166675.083333
881531160767.900.091.043.025.5434644.1444440.315508474.0419.71428626.142857
99160982005.6387.07.015.029.93477647.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketavg_recency_daysfrequencycant_returnsaux_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.011.016.0643946.00.2857146.0645.00000066.0
2960563717232421.522.02.010.011.70888912.00.1538460.0101.50000018.0
2961563817468137.0010.02.02.027.4000004.00.4000000.058.0000002.5
2962564913596697.045.02.010.04.1990367.00.2500000.0203.00000083.0
29635655148931237.859.02.014.016.9568492.00.6666670.0399.50000036.5
2964565912479473.2011.01.08.015.7733334.00.33333334.0382.00000030.0
2965568014126706.137.03.06.047.0753333.01.00000050.0169.3333335.0
29665686135211092.391.03.09.02.5112414.50.3000000.0244.333333145.0
2967569615060301.848.04.08.02.5153331.02.0000000.065.50000030.0
2968571512558269.967.01.05.024.5418186.00.285714196.0196.00000011.0